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  • 标题:Proof of concept for identifying cystic fibrosis from perspiration samples
  • 本地全文:下载
  • 作者:Zhenpeng Zhou ; Daniel Alvarez ; Carlos Milla
  • 期刊名称:Proceedings of the National Academy of Sciences
  • 印刷版ISSN:0027-8424
  • 电子版ISSN:1091-6490
  • 出版年度:2019
  • 卷号:116
  • 期号:49
  • 页码:24408-24412
  • DOI:10.1073/pnas.1909630116
  • 出版社:The National Academy of Sciences of the United States of America
  • 摘要:The gold standard for cystic fibrosis (CF) diagnosis is the determination of chloride concentration in sweat. Current testing methodology takes up to 3 h to complete and has recognized shortcomings on its diagnostic accuracy. We present an alternative method for the identification of CF by combining desorption electrospray ionization mass spectrometry and a machine-learning algorithm based on gradient boosted decision trees to analyze perspiration samples. This process takes as little as 2 min, and we determined its accuracy to be 98 ± 2% by cross-validation on analyzing 277 perspiration samples. With the introduction of statistical bootstrap, our method can provide a confidence estimate of our prediction, which helps diagnosis decision-making. We also identified important peaks by the feature selection algorithm and assigned the chemical structure of the metabolites by high-resolution and/or tandem mass spectrometry. We inspected the correlation between mild and severe CFTR gene mutation types and lipid profiles, suggesting a possible way to realize personalized medicine with this noninvasive, fast, and accurate method..
  • 关键词:desorption electrospray ionization ; mass spectrometry ; machine learning ; cystic fibrosis
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